Importance sampling for determining SRAM yield and optimization with statistical constraint

E.J.W. Maten, ter; O. Wittich; A. Di Bucchianico; T.S. Doorn; T.G.J. Beelen

doi:10.1007/978-3-642-22453-9_5

Importance sampling for determining SRAM yield and optimization with statistical constraint

E.J.W. Maten, ter
, O. Wittich
, A. Di Bucchianico
, T.S. Doorn
, T.G.J. Beelen

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

Samenvatting

Importance Sampling allows for efficient Monte Carlo sampling that also properly covers tails of distributions. From Large Deviation Theory we derive an optimal upper bound for the number of samples to efficiently sample for an accurate fail probability P fail = 10- 10. We apply this to accurately and efficiently minimize the access time of Static Random Access Memory (SRAM), while guaranteeing a statistical constraint on the yield target.

Originele taal-2	Engels
Titel	Scientific Computing in Electrical Engineering SCEE 2010
Redacteuren	B. Michielsen, J.R. Poirier
Plaats van productie	Berlin
Uitgeverij	Springer
Pagina's	39-47
ISBN van geprinte versie	978-3-642-22452-2
DOI's	https://doi.org/10.1007/978-3-642-22453-9_5
Status	Gepubliceerd - 2012
Evenement	Scientific Computing in Electrical Engineering, SCEE 2010 - Toulouse, Frankrijk Duur: 19 sep. 2010 → 24 sep. 2010 https://scee-conferences.org/

Publicatie series

Naam	Mathematics in Industry
Volume	16
ISSN van geprinte versie	1612-3956

Congres

Congres	Scientific Computing in Electrical Engineering, SCEE 2010
Land/Regio	Frankrijk
Stad	Toulouse
Periode	19/09/10 → 24/09/10
Ander	SCEE 2010
Internet adres	https://scee-conferences.org/

Toegang tot document

10.1007/978-3-642-22453-9_5

Citeer dit

Maten, ter, E. J. W., Wittich, O., Di Bucchianico, A., Doorn, T. S., & Beelen, T. G. J. (2012). Importance sampling for determining SRAM yield and optimization with statistical constraint. In B. Michielsen, & J. R. Poirier (editors), Scientific Computing in Electrical Engineering SCEE 2010 (blz. 39-47). (Mathematics in Industry; Vol. 16). Springer. https://doi.org/10.1007/978-3-642-22453-9_5

@inproceedings{f37f3b40506842e0a8fe14afc11661c7,

title = "Importance sampling for determining SRAM yield and optimization with statistical constraint",

abstract = "Importance Sampling allows for efficient Monte Carlo sampling that also properly covers tails of distributions. From Large Deviation Theory we derive an optimal upper bound for the number of samples to efficiently sample for an accurate fail probability P fail = 10- 10. We apply this to accurately and efficiently minimize the access time of Static Random Access Memory (SRAM), while guaranteeing a statistical constraint on the yield target.",

author = "\{Maten, ter\}, E.J.W. and O. Wittich and \{Di Bucchianico\}, A. and T.S. Doorn and T.G.J. Beelen",

year = "2012",

doi = "10.1007/978-3-642-22453-9\_5",

language = "English",

isbn = "978-3-642-22452-2",

series = "Mathematics in Industry",

publisher = "Springer",

pages = "39--47",

editor = "B. Michielsen and J.R. Poirier",

booktitle = "Scientific Computing in Electrical Engineering SCEE 2010",

address = "Germany",

note = "Scientific Computing in Electrical Engineering, SCEE 2010 ; Conference date: 19-09-2010 Through 24-09-2010",

url = "https://scee-conferences.org/",

}

Maten, ter, EJW, Wittich, O, Di Bucchianico, A, Doorn, TS & Beelen, TGJ 2012, Importance sampling for determining SRAM yield and optimization with statistical constraint. in B Michielsen & JR Poirier (reds.), Scientific Computing in Electrical Engineering SCEE 2010. Mathematics in Industry, vol. 16, Springer, Berlin, blz. 39-47, Scientific Computing in Electrical Engineering, SCEE 2010, Toulouse, Frankrijk, 19/09/10. https://doi.org/10.1007/978-3-642-22453-9_5

Importance sampling for determining SRAM yield and optimization with statistical constraint. / Maten, ter, E.J.W.; Wittich, O.; Di Bucchianico, A. et al.
Scientific Computing in Electrical Engineering SCEE 2010. reds. / B. Michielsen; J.R. Poirier. Berlin: Springer, 2012. blz. 39-47 (Mathematics in Industry; Vol. 16).

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

TY - GEN

T1 - Importance sampling for determining SRAM yield and optimization with statistical constraint

AU - Maten, ter, E.J.W.

AU - Wittich, O.

AU - Di Bucchianico, A.

AU - Doorn, T.S.

AU - Beelen, T.G.J.

PY - 2012

Y1 - 2012

N2 - Importance Sampling allows for efficient Monte Carlo sampling that also properly covers tails of distributions. From Large Deviation Theory we derive an optimal upper bound for the number of samples to efficiently sample for an accurate fail probability P fail = 10- 10. We apply this to accurately and efficiently minimize the access time of Static Random Access Memory (SRAM), while guaranteeing a statistical constraint on the yield target.

AB - Importance Sampling allows for efficient Monte Carlo sampling that also properly covers tails of distributions. From Large Deviation Theory we derive an optimal upper bound for the number of samples to efficiently sample for an accurate fail probability P fail = 10- 10. We apply this to accurately and efficiently minimize the access time of Static Random Access Memory (SRAM), while guaranteeing a statistical constraint on the yield target.

U2 - 10.1007/978-3-642-22453-9_5

DO - 10.1007/978-3-642-22453-9_5

M3 - Conference contribution

SN - 978-3-642-22452-2

T3 - Mathematics in Industry

SP - 39

EP - 47

BT - Scientific Computing in Electrical Engineering SCEE 2010

A2 - Michielsen, B.

A2 - Poirier, J.R.

PB - Springer

CY - Berlin

T2 - Scientific Computing in Electrical Engineering, SCEE 2010

Y2 - 19 September 2010 through 24 September 2010

ER -

Importance sampling for determining SRAM yield and optimization with statistical constraint

Samenvatting

Publicatie series

Congres

Toegang tot document

Vingerafdruk

Citeer dit